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Effect of cutting parameters on tool wear under minimum quantity cooling lubrication (MQCL) conditions

  • Niancong LiuEmail author
  • Chengli Zheng
  • Daiyang Xiang
  • Hao Huang
  • Jin Wang
ORIGINAL ARTICLE
  • 140 Downloads

Abstract

This paper presents the effect of cutting parameters on tool wear under minimum quantity cooling lubrication (MQCL) conditions. Turning AISI 304 stainless steel was carried out using VAMF-1 environmentally friendly cutting fluid under MQCL conditions, for which 27 groups of all-factor experiments were utilized. The tool wear results showed that the most effective parameter was the cutting speed (relative contribution of 46.725%), followed by the feed rate (relative contribution of 28.120%). According to the tool vibration results, the cutting speed was the most important parameter, followed by the feed rate and the cutting depth. During low-speed cutting, a larger feed rate and cutting depth could be selected, for which adhesive wear is the prevailing mechanism. As the cutting speed increased, the built-up edge (BUE) near the cutting edge disappeared and the adhesives fell off; diffusion wear was the main mechanism in this case. In addition, a prediction model was constructed with the objective of surface finish and tool wear, and the results were optimized by an improved fruit fly optimization algorithm (FOA). The verification experiment results showed that the prediction errors of VB and Ra were 2.15% and 6.48%, respectively. Moreover, MQCL achieved better surface quality and lower tool wear than minimum quantity lubrication (MQL) and dry cutting under the optimized parameters.

Keywords

Tool wear MQCL AISI304 Cutting parameter Tool vibration FOA optimization 

Notes

Funding information

This work was supported by Sichuan Science and Technology Program of China under grant (2019YF0385); the Chengdu Science and Technology Program under grant (2015-NY02-00285-NC).

References

  1. 1.
    Zhang GQ, To S, Wu XY, Lou Y (2018) Steady tool wear and its influence on tool geometry in ultra-precision fly cutting of CuZn30. Int J Adv Manuf Technol 101(9–12):2653–2662.  https://doi.org/10.1007/s00170-018-3111-6 CrossRefGoogle Scholar
  2. 2.
    Chinchanikar S, Choudhury SK (2013) Wear behaviors of single-layer and multi-layer coated carbide inserts in high speed machining of hardened AISI 4340 steel. J Mech Sci Technol 27(5):1451–1459.  https://doi.org/10.1007/s12206-013-0325-2 CrossRefGoogle Scholar
  3. 3.
    Naskar A, Chattopadhyay AK (2018) Investigation on flank wear mechanism of CVD and PVD hard coatings in high speed dry turning of low and high carbon steel. Wear 396:98–106.  https://doi.org/10.1016/j.wear.2017.11.010 CrossRefGoogle Scholar
  4. 4.
    Hegab H, Umer U, Deiab I, Kishawy H (2018) Performance evaluation of Ti–6Al–4V machining using nano-cutting fluids under minimum quantity lubrication. Int J Adv Manuf Technol 95(9-12):4229–4241.  https://doi.org/10.1007/s00170-017-1527-z CrossRefGoogle Scholar
  5. 5.
    Singh BK, Mondal B, Mandal N (2016) Machinability evaluation and desirability function optimization of turning parameters for Cr2O3 doped zirconia toughened alumina (Cr-ZTA) cutting insert in high speed machining of steel. Ceram Int 42(2):3338–3350.  https://doi.org/10.1016/j.ceramint.2015.10.128 CrossRefGoogle Scholar
  6. 6.
    Zheng GM, Xu RF, Cheng X, Zhao GY, Li L, Zhao J (2018) Effect of cutting parameters on wear behavior of coated tool and surface roughness in high-speed turning of 300 M. Measurement 125:99–108.  https://doi.org/10.1016/j.measurement.2018.02.018 CrossRefGoogle Scholar
  7. 7.
    Suresh R, Basavarajappa S, Samuel GL (2012) Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool. Measurement 45(7):1872–1884.  https://doi.org/10.1016/j.measurement.2012.03.024 CrossRefGoogle Scholar
  8. 8.
    Viswanathan R, Ramesh S, Subburam V (2018) Measurement and optimization of performance characteristics in turning of Mg alloy under dry and MQL conditions. Measurement 120:107–113.  https://doi.org/10.1016/j.measurement.2018.02.018 CrossRefGoogle Scholar
  9. 9.
    Sharma AK, Tiwari AK, Dixit AR (2016) Effects of minimum quantity lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: a comprehensive review. J Clean Prod 127:1–18.  https://doi.org/10.1016/j.jclepro.2016.03.146 CrossRefGoogle Scholar
  10. 10.
    Thakur DG, Ramamoorthy B, Vijayaraghavan L (2010) Investigation and optimization of lubrication parameters in high speed turning of superalloy Inconel 718. Int J Adv Manuf Technol 50(5-8):471–478.  https://doi.org/10.1007/s00170-010-2538-1 CrossRefGoogle Scholar
  11. 11.
    Singh T, Dureja JS, Dogra M, Bhatti MS (2018) Environment friendly machining of Inconel 625 under nano-fluid minimum quantity lubrication (NMQL). Int J Adv Manuf Technol 19(11):1689–1697.  https://doi.org/10.1007/s12541-018-0196-7 CrossRefGoogle Scholar
  12. 12.
    Gupta MK, Sood PK (2017) Machining comparison of aerospace materials considering minimum quantity cutting fluid: a clean and green approach. Proc IME C J Mech Eng Sci 231(8):1445–1464.  https://doi.org/10.1177/0954406216684158 CrossRefGoogle Scholar
  13. 13.
    Singh RK, Sharma AK, Dixit AR, Tiwari AK, Pramanik A, Mandal A (2017) Performance evaluation of alumina-graphene hybrid nano-cutting fluid in hard turning. J Clean Prod 162:830–845.  https://doi.org/10.1016/j.jclepro.2017.06.104 CrossRefGoogle Scholar
  14. 14.
    Araujo AS, Sales WF, da Silva RB, Costa ES, Machado AR (2017) Lubri-cooling and tribological behavior of vegetable oils during milling of AISI 1045 steel focusing on sustainable manufacturing. J Clean Prod 156:635–647.  https://doi.org/10.1016/j.jclepro.2017.04.061 CrossRefGoogle Scholar
  15. 15.
    Pervaiz S, Rashid A, Deiab I, Nicolescu CM (2016) An experimental investigation on effect of minimum quantity cooling lubrication (MQCL) in machining titanium alloy (Ti6Al4V). Int J Adv Manuf Technol 87(5–8):1371–1386.  https://doi.org/10.1007/s00170-016-8969-6 CrossRefGoogle Scholar
  16. 16.
    Maruda RW, Krolczyk GM, Feldshtein E, Nieslony P, Tyliszczak B, Pusavec F (2017) Tool wear characterizations in finish turning of AISI 1045 carbon steel for MQCL conditions. Wear 372:54–67.  https://doi.org/10.1016/j.wear.2016.12.006 CrossRefGoogle Scholar
  17. 17.
    Benjamin DM, Sabarish VN, Hariharan MV, Raj DS (2018) On the benefits of sub-zero air supplemented minimum quantity lubrication systems: an experimental and mechanistic investigation on end milling of Ti-6-Al-4-V alloy. Tribol Int 119:464–473.  https://doi.org/10.1016/j.triboint.2017.11.021 CrossRefGoogle Scholar
  18. 18.
    Zhou FJ (2014) A new analytical tool-chip frictionmodel in dry cutting. Int J Adv Manuf Technol 70(1-4):309–319.  https://doi.org/10.1007/s00170-013-5271-8 CrossRefGoogle Scholar
  19. 19.
    Sharma AK, Tiwari AK, Dixit AR, Singh RK (2017) Novel uses of alumina-MoS2 hybrid nanoparticle enriched cutting fluid in hard turning of AISI 304 steel. J Manuf Process 30:467–482.  https://doi.org/10.1016/j.jmapro.2017.10.016 CrossRefGoogle Scholar
  20. 20.
    Sharma AK, Katiyar JK, Bhaumik S, Roy S (2019) Influence of alumina/MWCNT hybrid nanoparticle additives on tribological properties of lubricants in turning operations. Friction 7(2):153–168.  https://doi.org/10.1007/s40544-018-0199-5 CrossRefGoogle Scholar
  21. 21.
    Huang SQ, Lv T, Wang MH, Xu XF (2018) Effects of machining and oil mist parameters on electrostatic minimum quantity lubrication-EMQL turning process. Int J Precis Eng Manuf Green Technol 5(2):317–326.  https://doi.org/10.1007/s40684-018-0034-5 CrossRefGoogle Scholar
  22. 22.
    Kara F, Ozturk B (2019) Comparison and optimization of PVD and CVD method on surface roughness and flank wear in hard-machining of DIN 1.2738 mold steel. Sens Rev 39(1):24–33.  https://doi.org/10.1108/SR-12-2017-0266 CrossRefGoogle Scholar
  23. 23.
    Hashemifa R, Ddehkordi SH, Almassi M, Borghei AM (2013) Simulation of small diesel engine vibration using artificial neural network. Int J Agric Crop Sci 5(18):2084–2090Google Scholar
  24. 24.
    Abukhshim NA, Mativenga PT, Sheikh MA (2005) Investigation of heat partition in high speed turning of high strength alloy steel. Int J Mach Tools Mauf 45(15):1687–1695.  https://doi.org/10.1016/j.ijmachtools.2005.03.008 CrossRefGoogle Scholar
  25. 25.
    Sasahara H, Satake K, Takahashi W, Goto M, Yamamoto H (2017) The effect of oil mist supply on cutting point temperature and tool wear in driven rotary cutting. Precis Eng-J Int Soc Precis Eng Nanotechnol 48:158–163.  https://doi.org/10.1016/j.precisioneng.2016.11.016 CrossRefGoogle Scholar
  26. 26.
    Yucel E, Gunay M (2013) Modelling and optimization of the cutting conditions in hard turning of high-alloy white cast iron (ni-hard). Proc IME C J Mech Eng Sci 227(10):2280–2290.  https://doi.org/10.1177/0954406212471755 CrossRefGoogle Scholar
  27. 27.
    Chinchanikar S, Choudhury SK (2013) Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel: an optimization approach. Measurment 46(4):1572–1584.  https://doi.org/10.1016/j.measurement.2012.11.032 CrossRefGoogle Scholar
  28. 28.
    Davoodi B, Eskandari B (2015) Tool wear mechanisms and multi-response optimization of tool life and volume of material removed in turning of N-155 iron–nickel-base superalloy using RSM. Measurement 68:286–294.  https://doi.org/10.1016/j.measurement.2015.03.006 CrossRefGoogle Scholar
  29. 29.
    Mir MJ, Wani MF (2017) Performance evaluation of PCBN, coated carbide and mixed ceramic inserts in finish-turning of AISI D2 steel. J Tribol 14:10–31Google Scholar
  30. 30.
    Fan Y, Hao Z, Zheng M, Yang S (2016) Wear characteristics of cemented carbide tool in drymachining Ti-6Al-4V. Mach Sci Technol 20(2):249–261CrossRefGoogle Scholar
  31. 31.
    Kara F (2017) Taguchi optimization of surface roughness and flank wear during the turning of DIN 1.2344 tool steel. Mater Test 59(10):903–908.  https://doi.org/10.3139/120.111085 CrossRefGoogle Scholar
  32. 32.
    Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl-Based Syst 26:69–74.  https://doi.org/10.1016/j.knosys.2011.07.001 CrossRefGoogle Scholar
  33. 33.
    Wang L, Shi YL, Liu S (2015) An improved fruit fly optimization algorithm and its application to joint replenishment problems. Expert Syst Appl 42(9):4310–4323.  https://doi.org/10.1016/j.eswa.2015.01.048 CrossRefGoogle Scholar
  34. 34.
    Lv SX, Zeng YR, Wang L (2018) An effective fruit fly optimization algorithm with hybrid information exchange and its applications. Int J Mach Learn Cybern 9(10):1623–1648.  https://doi.org/10.1007/s13042-017-0669-5 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Niancong Liu
    • 1
    Email author
  • Chengli Zheng
    • 1
  • Daiyang Xiang
    • 1
  • Hao Huang
    • 1
  • Jin Wang
    • 1
  1. 1.Department of Nuclear Technology and Automation EngineeringChengdu University of TechnologyChengduChina

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